Dialogue systems are a popular natural language processing (NLP) task as it is promising in
real-life applications. It is also a complicated task since many NLP tasks deserving study are …

Recommender systems are software applications that help users to find items of interest in
situations of information overload. Current research often assumes a one-shot interaction …

Practitioners increasingly use machine learning (ML) models, yet models have become
more complex and harder to understand. To understand complex models, researchers have …

Even though machine learning has become the major scene in dialogue research
community, the real breakthrough has been blocked by the scale of data available. To …

This tutorial surveys neural approaches to conversational AI that were developed in the last
few years. We group conversational systems into three categories:(1) question answering …

Dialogue systems have attracted more and more attention. Recent advances on dialogue
systems are overwhelmingly contributed by deep learning techniques, which have been …

There has been growing interest in using neural networks and deep learning techniques to
create dialogue systems. Conversational recommendation is an interesting setting for the …

In this paper, we survey the methods and concepts developed for the evaluation of dialogue
systems. Evaluation, in and of itself, is a crucial part during the development process. Often …

Similar to humans, RL agents use interactive learning to successfully obtain satisfactory
decision strategies. However, in many cases, it is desirable to learn directly from …

Semantic processing is a fundamental research domain in computational linguistics. In the
era of powerful pre-trained language models and large language models, the advancement …